Combining Multiple Sets of Rules for Improving Classification Via Measuring Their Closenesses

Yaxin Bi, Shengli Wu, Xuming Huang, Gongde Guo

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we propose a new method for measuring the closeness of multiple sets of rules that are combined using Dempster’s rule of combination to improve classification performance. The closeness provides an insight into combining multiple sets of rules in classification − in what circumambience the performance of combinations of some sets of rules using Dempster’s rule is better than that of others. Experiments have been carried out over the 20-newsgroups benchmark data collection, and the empirical results show that when the closeness between two sets of rules is higher than that of others, the performance of its combination using Dempster’s rule is better than the others.
Original languageEnglish
Title of host publicationPRICAI 2006: Trends in Artificial Intelligence Lecture Notes in Computer Science
PublisherSpringer
Pages1068-1072
ISBN (Print)978-3-540-36667-6
Publication statusPublished - 2006

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    Bi, Y., Wu, S., Huang, X., & Guo, G. (2006). Combining Multiple Sets of Rules for Improving Classification Via Measuring Their Closenesses. In PRICAI 2006: Trends in Artificial Intelligence Lecture Notes in Computer Science (pp. 1068-1072). Springer.